Language-Instructed Vision Embeddings for Controllable and Generalizable Perception
Vision foundation models are typically trained as static feature extractors, placing the burden of task adaptation onto large downstream models. We propose an alternative paradigm: instead of solely feeding visual features into language models, we use language itself to dynamically guide the vision encoder. Our method, Language-Instructed Vision Embeddings (LIVE), leverages language as high-level guidance to produce task-centric embeddings at inference time, removing the need for task-specific retraining. This enables the encoder to focus on contextually relevant aspects of the input, yielding more controllable and generalizable representations. Empirically, LIVE reduces visual hallucinat...
| Authors | Chengzhi Mao, Xudong Lin, Wen-Sheng Chu |
|---|---|
| Primary category | cs.CV |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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